Using machine learning to improve national lake depth predictions
2024-11-19
FENZ Lakes
Depth contours
Bathymetry raster
Estimated hypsographic curve
Estimated hypsographic curve
Hypsographic summary
| Variable | Value |
|---|---|
| Max depth | 90.25 |
| Mean depth | 45.43 |
| Volume development index | 1.51 |
| Area (ha) | 1063.27 |
| Shoreline length (km) | 23.40 |
| Shoreline development index | 2.04 |
Development of Volume, \(D_V\) (Hutchinson 1957) is a measure of departure of the shape of the lake basin from that of a cone calculated using the maximum depth \(Z_{max}\) and the average depth \(\bar{Z}\) :
\[D_V = \frac{3 \times \bar{Z}}{Z_{max}}\]
For the majority of lakes, DV >1 (i.e. a conical depression). DV is greatest in shallow lakes with flat bottoms.
Figure 9: Distribution of volume development (DV) by geomorphic type.
| Source | N | Bias | RMSE | R2 |
|---|---|---|---|---|
| FENZ | 183 | -0.79 | 44.64 | 0.70 |
| GloBATHY | 119 | 20.93 | 72.68 | 0.43 |
| ML model | 190 | -0.49 | 19.17 | 0.95 |
| Source | N | Bias | RMSE | R2 |
|---|---|---|---|---|
| FENZ | 155 | 6.24 | 21.59 | 0.71 |
| Hydrolakes | 91 | 7.27 | 22.48 | 0.79 |
| ML model | 155 | -0.15 | 7.80 | 0.97 |
Bathymetric data from 156 lakes were digitised and collated for use in this study.
Using a machine learning model, we were able to predict maximum lake depth from morphometric data with an \(r^2\) = 0.95 and mean lake depth with an \(r^2\) = 0.97.
This was substantially better than the FENZ dataset, which had an \(r^2\) = 0.7 for max depth and 0.71 for mean depth.
